Which algorithm is used for solving temporal probabilistic reasoning?

To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model.

The correct answer to this question would be the “Hidden Markov Models (HMMs)” algorithm.

Hidden Markov Models are commonly used for solving temporal probabilistic reasoning problems, particularly in scenarios where there is uncertainty about the state of a system over time. HMMs are a powerful statistical tool for modeling sequences of observations or events where the underlying system’s state is hidden but can be inferred probabilistically based on the observed data. They have applications in various fields including speech recognition, natural language processing, bioinformatics, and more.